Thin-film Longitudinal Intrafascicular Electrodes Research Articles

Selective chronic recording in small nerve fascicles of sciatic nerve with carbon nanotube yarns in rats

Objective. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory signals. Approach. In this study, we analyze the chronic recording capabilities of carbon nanotube yarn electrodes in the peripheral somatic nervous system. The electrodes were surgically implanted in the sciatic nerve’s three individual fascicles in rats, enabling the recording of neural activity during gait. Signal-to-noise ratio (SNR) and information theory were employed to analyze the data, demonstrating the superior recording capabilities of the electrodes. Flat interface nerve electrode and thin-film longitudinal intrafascicular electrode electrodes were used as a references to assess the results from SNR and information theory analysis. Main results. The electrodes exhibited the ability to record chronic signals with SNRs reaching as high as 15 dB, providing 12 bits of information for the sciatic nerve, a significant improvement over previous methods. Furthermore, the study revealed that the SNR and information content of the neural signals remained consistent over a period of 12 weeks across three different fascicles, indicating the stability of the interface. The signals recorded from these electrodes were also analyzed for selectivity using information theory metrics, which showed an information sharing of approximately 1.4 bits across the fascicles. Significance. The ability to safely and reliably record from multiple fascicles of different nerves simultaneously over extended periods of time holds substantial implications for the field of neural and rehabilitation engineering. This advancement addresses the limitation of current nerve interface technologies and opens up new possibilities for enhancing neural rehabilitation and control.

Estimation of the Electrode-Fiber Bioelectrical Coupling From Extracellularly Recorded Single Fiber Action Potentials.

Selective peripheral neural interfaces are currently capable of detecting minute electrical signals from nearby nerve fibers as single fiber action potential (SFAP) waveforms. Each detected single unit has a distinct shape originating from the unique bioelectrical coupling that exists between the neuroprosthetic electrode, the nerve fiber and the extracellular milieu. The bioelectrical coupling manifests itself as a series of low-pass Bessel filters acting on the action currents along the nerve fiber. Here, we present a method to estimate the electrode-fiber bioelectrical coupling through a quantitative analysis of the spectral distribution of the single units extracellularly recorded with the thin-film longitudinal intrafascicular electrode (tfLIFE) in an in vivo mammalian peripheral nerve animal model. The bioelectrical coupling estimate is an estimate of the electrode sensitivity function traversed by the nerve fiber, suggesting that it is as a means to directly measure the spatial relationship between the nerve fiber and electrode. It not only reflects a shape change to the SFAP, but has implications for in situ nerve fiber location tracking, in situ diagnostics of nerves and neuroproshetic electrodes, and assessment of the biocompatibility of neural interfaces and the health of the reporting nerve fibers.

Bioelectric interfaces for the peripheral nervous system.

The peripheral nervous system (PNS) is an attractive target for those developing neural interfaces as an access point to the information flow coursing within our bodies. A successful neural interface could not only offer the means to understand basic neurophysiological mechanisms, such as how the body accomplishes complex coordinated control of multi degree of freedom body segments, but also could serve as the means of delivering treatment or therapies to restore physiological functions lost due to injury or disease. Our work in the development of such a neural interface focuses upon multi-microelectrode devices that are placed within the body of the nerve fascicle; mulit-channel intra-fascicular devices called the thin-film Longitudinal Intra-Fascicular Electrode (tfLIFE) and the Transversely Implanted Multi-Electrode (TIME). These structures provide high resolution access to the PNS and have demonstrated promise in animal work as well as in preliminary sub-acute work in human volunteers. However, work remains to improve upon their longevity and biocompatibility before full translation to clinical work can occur.

Correlation between muscular and nerve signals responsible for hand grasping in non-human primates.

Neuroprosthetic devices that interface with the nervous system to restore functional motor activity offer a viable alternative to nerve regeneration, especially in proximal nerve injuries like brachial plexus injuries where muscle atrophy may set in before nerve re-innervation occurs. Prior studies have used control signals from muscle or cortical activity. However, nerve signals are preferred in many cases since they permit more natural and precise control when compared to muscle activity, and can be accessed with much lower risk than cortical activity. Identification of nerve signals that control the appropriate muscles is essential for the development of such a `bionic link'. Here we examine the correlation between muscle and nerve signals responsible for hand grasping in the M. fascicularis. Simultaneous recordings were performed using a 4-channel thin-film longitudinal intra-fascicular electrode (tf-LIFE) and 9 bipolar endomysial muscle electrodes while the animal performed grasping movements. We were able to identify a high degree of correlation (r > 0.6) between nerve signals from the median nerve and movement-dependent muscle activity from the flexor muscles of the forearm, with a delay that corresponded to 25 m/s nerve conduction velocity. The phase of the flexion could be identified using a wavelet approximation of the ENG. This result confirms this approach for a future neuroprosthetic device for the treatment of peripheral nerve injuries.

Stimulation Selectivity of the “Thin-Film Longitudinal Intrafascicular Electrode” (tfLIFE) and the “Transverse Intrafascicular Multi-Channel Electrode” (TIME) in the Large Nerve Animal Model

Neural prostheses are limited by the availability of peripheral neural electrodes to record the user's intention or provide sensory feedback through functional electrical stimulation. Our objective was to compare the ability of the novel “transverse intrafascicular multi-channel electrode” (TIME) and an earlier generation “thin-film longitudinal intrafascicular electrode” (tfLIFE) to selectively stimulate nerve fascicles and activate forelimb muscles in pigs. TIME was designed to access a larger subpopulation of fascicles than tfLIFE and should therefore be able to selectively activate a larger number of muscles. Electrodes were implanted in the median nerve, and sequential electric stimulation was applied to individual contacts. The compound muscle action potentials of seven muscles were recorded to quantify muscle recruitment. As expected, TIME was able to recruit more muscles with higher selectivity than tfLIFE (significant difference when comparing the performance of an entire electrode); a similar activation current was used (no significant difference). Histological analysis revealed that electrodes were located between fascicles, which influenced the selectivity and activation current level. In conclusion, TIME is a viable neural interface for selective activation of multiple fascicles in human-sized nerves that may assist to pave the way for future neuroprosthesis applications.

Stationary wavelet transform and higher order statistical analyses of intrafascicular nerve recordings

Nerve signals were recorded from the sciatic nerve of the rabbits in the acute experiments with multi-channel thin-film longitudinal intrafascicular electrodes. 5.5 s sequences of quiescent and high-level nerve activity were spectrally decomposed by applying a ten-level stationary wavelet transform with the Daubechies 10 (Db10) mother wavelet. Then, the statistical distributions of the raw and subband-decomposed sequences were estimated and used to fit a fourth-order Pearson distribution as well as check for normality. The results indicated that the raw and decomposed background and high-level nerve activity distributions were nearly zero-mean and non-skew. All distributions with the frequency content above 187.5 Hz were leptokurtic except for the first-level decomposition representing frequencies in the subband between 12 and 24 kHz, which was Gaussian. This suggests that nerve activity acts to change the statistical distribution of the recording. The results further demonstrated that quiescent recording contained a mixture of an underlying pink noise and low-level nerve activity that could not be silenced. The signal-to-noise ratios based upon the standard deviation (SD) and kurtosis were estimated, and the latter was found as an effective measure for monitoring the nerve activity residing in different frequency subbands. The nerve activity modulated kurtosis along with SD, suggesting that the joint use of SD and kurtosis could improve the stability and detection accuracy of spike-detection algorithms. Finally, synthesizing the reconstructed subband signals following denoising based upon the higher order statistics of the subband-decomposed coefficient sequences allowed us to effectively purify the signal without distorting spike shape.

Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control

Background. Interfacing an amputee’s upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. Objective. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Methods. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Results. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (α/β band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored α band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Conclusions Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and mitigation of PLP.

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

BackgroundThe restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting.MethodsThin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm.ResultsThe results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm.ConclusionsThese results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

Spike sorting of muscle spindle afferent nerve activity recorded with thin-film intrafascicular electrodes.

Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.

Interpretation of Muscle Spindle Afferent Nerve Response to Passive Muscle Stretch Recorded With Thin-Film Longitudinal Intrafascicular Electrodes

In this study, we explored the feasibility of estimating muscle length in passive conditions by interpreting nerve responses from muscle spindle afferents recorded with thin-film longitudinal intrafascicular electrodes. Afferent muscle spindle response to passive stretch was recorded in ten acute rabbit experiments. A newly proposed first-order model of muscle spindle response to passive sinusoidal muscle stretch manages to capture the relationship between afferent neural firing rate and muscle length. We demonstrate that the model can be used to track random motion trajectories with bandwidth from 0.1 to 1 Hz over a range of 4 mm with a muscle length estimation error of 0.3 mm (1.4 degrees of joint angle). When estimation is performed using four-channel ENG there is a 50% reduction in estimate variation, compared to using single-channel recordings.

On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes

Among the possible interfaces with the peripheral nervous system (PNS), intraneural electrodes represent an interesting solution for their potential advantages such as the possibility of extracting spikes from electroneurographic (ENG) signals. Their use could increase the precision and the amount of information which can be detected with respect to other processing methods.In this study, in order to verify this assumption, thin-film longitudinal intrafascicular electrodes (tfLIFE) were implanted in the sciatic nerve of rabbits. Various sensory stimuli were applied to the hind limb of the animal and the elicited ENG signals were recorded using the tfLIFEs. These signals were processed to determine whether the different types of information can be decoded. Signals were wavelet denoised and spike sorted. Support vector machines were trained to use the spike waveforms found to infer the stimulus applied to the rabbit. This approach was also compared with previously used ENG-processing methods.The results indicate that the combination of wavelet denoising and spike sorting techniques can increase the amount of information extractable from ENG signals recorded with intraneural electrodes. This strategy could allow the development of more effective closed-loop neuroprostheses and hybrid bionic systems connecting the human nervous system with artificial devices.